Person:
Jumbo, M.B

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Jumbo
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M.B
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Jumbo, M.B

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Now showing 1 - 10 of 11
  • Development and deployment of elite maize lines and hybrids resistant to Maize Lethal Necrosis
    (CIMMYT, 2019) Beyene, Y.; Suresh, L.M.; Gowda, M.; Makumbi, D.; Olsen, M.; Jumbo, M.B; Regasa, M.W.; Mugo, S.N.; Prasanna, B.M.
    Publication
  • CIMMYT Eastern Africa Intermediate Maturity Maize Breeding Program
    (CIMMYT, 2019) Beyene, Y.; Mugo, S.N.; Jumbo, M.B; Regasa, M.W.; Gowda, M.; Suresh, L.M.; Chaikam, V.; Bruce, A.Y.; Olsen, M.; Prasanna, B.M.; Crossa, J.; Chavangi, A.; Gichobi, P.
    Publication
  • Genetic architecture of maize chlorotic mottle virus and maize lethal necrosis through GWAS, linkage analysis and genomic prediction in tropical maize germplasm
    (Springer, 2019) Sitonik, C.; Suresh, L.M.; Beyene, Y.; Olsen, M.; Makumbi, D.; Kiplagat, O.; Das, B.; Jumbo, M.B; Mugo, S.N.; Crossa, J.; Tarekegne, A.T.; Prasanna, B.M.; Gowda, M.
    Maize lethal necrosis (MLN) is a serious threat to the food security of maize-growing smallholders in sub-Saharan Africa. The ability of the maize chlorotic mottle virus (MCMV) to interact with other members of the Potyviridae causes severe yield losses in the form of MLN. The objective of the present study was to gain insights and validate the genetic architecture of resistance to MCMV and MLN in maize. We applied linkage mapping to three doubled-haploid populations and a genome-wide association study (GWAS) on 380 diverse maize lines. For all the populations, phenotypic variation for MCMV and MLN was significant, and heritability was moderate to high. Linkage mapping revealed 13 quantitative trait loci (QTLs) for MCMV resistance and 12 QTLs conferring MLN resistance. One major-effect QTL, qMCMV3-108/qMLN3-108, was consistent across populations for both MCMV and MLN resistance. Joint linkage association mapping (JLAM) revealed 18 and 21 main-effect QTLs for MCMV and MLN resistance, respectively. Another major-effect QTL, qMCMV6-17/qMLN6-17, was detected for both MCMV and MLN resistance. The GWAS revealed a total of 54 SNPs (MCMV-13 and MLN-41) significantly associated (P ≤ 5.60 × 10−05) with MCMV and MLN resistance. Most of the GWAS-identified SNPs were within or adjacent to the QTLs detected through linkage mapping. The prediction accuracy for within populations as well as the combined populations is promising; however, the accuracy was low across populations. Overall, MCMV resistance is controlled by a few major and many minor-effect loci and seems more complex than the genetic architecture for MLN resistance.
    Publication
  • CIMMYT GMP Africa marker applications: The Carpenter’s Dilemma (…while waiting for the concrete to dry)
    (CIMMYT, 2018) Olsen, M.; Gowda, M.; Jumbo, M.B; Ogugo, V.; Ng’ang’a, M.; Murithi, A.; Tadesse, B.; Beyene, Y.; Xuecai Zhang; Dreher, K.; Shibin Gao; Crossa, J.; Jones, L.; Robbins, K.
    Publication
  • Accelerated development and deployment of elite maize hybrids tolerant to maize lethal necrosis, a major disease of maize in eastern Africa
    (2018) Beyene, Y.; Gowda, M.; Olsen, M.; Jumbo, M.B; Makumbi, D.; Regasa, M.W.; Mugo, S.N.; Prasanna, B.M.
    Publication
  • Advances in Breeding for Resistance/Tolerance to MLN
    (2018) Suresh, L.M.; Beyene, Y.; Makumbi, D.; Jumbo, M.B; Gowda, M.; Regasa, M.W.; Olsen, M.; Mugo, S.N.; Prasanna, B.M.
    Publication
  • Development and deployment of elite maize hybrids for effectively tackling the Maize Lethal Necrosis (MNL), a major disease of maize in eastern Africa
    (CIMMYT, 2018) Beyene, Y.; Gowda, M.; Prasanna, B.M.; Mugo, S.N.; Regasa, M.W.; Makumbi, D.; Jumbo, M.B; Olsen, M.
    Publication
  • Discovery and validation of genomic regions associated with resistance to maize lethal necrosis in four biparental populations
    (Springer Verlag, 2018) Gowda, M.; Beyene, Y.; Makumbi, D.; Semagn, K.; Olsen, M.; Jumbo, M.B; Das, B.; Mugo, S.N.; Suresh, L.M.; Prasanna, B.M.
    In sub-Saharan Africa, maize is the key determinant of food security for smallholder farmers. The sudden outbreak of maize lethal necrosis (MLN) disease is seriously threatening the maize production in the region. Understanding the genetic basis of MLN resistance is crucial. In this study, we used four biparental populations applied linkage mapping and joint linkage mapping approaches to identify and validate the MLN resistance-associated genomic regions. All populations were genotyped with low to high density markers and phenotyped in multiple environments against MLN under artificial inoculation. Phenotypic variation for MLN resistance was significant and heritability was moderate to high in all four populations for both early and late stages of disease infection. Linkage mapping revealed three major quantitative trait loci (QTL) on chromosomes 3, 6, and 9 that were consistently detected in at least two of the four populations. Phenotypic variance explained by a single QTL in each population ranged from 3.9% in population 1 to 43.8% in population 2. Joint linkage association mapping across three populations with three biometric models together revealed 16 and 10 main effect QTL for MLN-early and MLN-late, respectively. The QTL identified on chromosomes 3, 5, 6, and 9 were consistent with the QTL identified by linkage mapping. Ridge regression best linear unbiased prediction with five-fold cross-validation revealed high accuracy for prediction across populations for both MLN-early and MLN-late. Overall, the study discovered and validated the presence of major effect QTL on chromosomes 3, 6, and 9 which can be potential candidates for marker-assisted breeding to improve the MLN resistance.
    Publication
  • Genome‑wide association and genomic prediction of resistance to maize lethal necrosis disease in tropical maize germplasm
    (Springer, 2015) Gowda, M.; Das, B.; Makumbi, D.; Babu, R.; Semagn, K.; Mahuku, G.; Olsen, M.; Jumbo, M.B; Beyene, Y.; Prasanna, B.M.
    The maize lethal necrosis disease (MLND) caused by synergistic interaction of Maize chlorotic mottle virus and Sugarcane mosaic virus, and has emerged as a serious threat to maize production in eastern Africa since 2011. Our objective was to gain insights into the genetic architecture underlying the resistance to MLND by genome-wide association study (GWAS) and genomic selection. We used two association mapping (AM) panels comprising a total of 615 diverse tropical/subtropical maize inbred lines. All the lines were evaluated against MLND under artificial inoculation. Both the panels were genotyped using genotyping-by-sequencing. Phenotypic variation for MLND resistance was significant and heritability was moderately high in both the panels. Few promising lines with high resistance to MLND were identified to be used as potential donors. GWAS revealed 24 SNPs that were significantly associated (P < 3 × 10−5) with MLND resistance. These SNPs are located within or adjacent to 20 putative candidate genes that are associated with plant disease resistance. Ridge regression best linear unbiased prediction with five-fold cross-validation revealed higher prediction accuracy for IMAS-AM panel (0.56) over DTMA-AM (0.36) panel. The prediction accuracy for both within and across panels is promising; inclusion of MLND resistance associated SNPs into the prediction model further improved the accuracy. Overall, the study revealed that resistance to MLND is controlled by multiple loci with small to medium effects and the SNPs identified by GWAS can be used as potential candidates in MLND resistance breeding program.
    Publication
  • Genomic prediction in a large African maize population
    (Crop Science Society of America (CSSA), 2017) Edriss, V.; Yanxin Gao; Xuecai Zhang; Jumbo, M.B; Makumbi, D.; Olsen, M.; Crossa, J.; Packard, K.C.; Jannink, J.L.
    Genomic prediction (GP) combines genomewide marker data with phenotypic data in a training population to predict the genomic estimated breeding values of untested individuals in a relevant testing population. Our objective was to evaluate the effects of p
    Publication